


'''simple docstring'''



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A__						:   Dict				=    16
A__						:   List[Any]				=    32



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Accelerator ,       UpperCAmelCase_       :							int = 16				)	->						Union[str, Any]:
     __lowerCamelCase  :      Dict														=						AutoTokenizer.from_pretrained('bert-base-cased'				)
     __lowerCamelCase  :      List[Any]														=						load_dataset('glue' ,       'mrpc'				)

     def tokenize_function(UpperCAmelCase_       :							Any				):
          # max_length=None => use the model max length (it's actually the default)
          __lowerCamelCase  :      Dict														=						tokenizer(examples['sentence1'] ,       examples['sentence2'] ,       truncation=__snake_case ,       max_length=__snake_case				)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          __lowerCamelCase  :      str														=						datasets.map(
              __snake_case ,       batched=__snake_case ,       remove_columns=['idx', 'sentence1', 'sentence2'] ,       )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __lowerCamelCase  :      Optional[Any]														=						tokenized_datasets.rename_column('label' ,       'labels'				)

     def collate_fn(UpperCAmelCase_       :							List[Any]				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __lowerCamelCase  :      List[Any]														=						1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __lowerCamelCase  :      Dict														=						16
          elif accelerator.mixed_precision != "no":
               __lowerCamelCase  :      int														=						8
          else:
               __lowerCamelCase  :      List[str]														=						None

          return tokenizer.pad(
              __snake_case ,       padding='longest' ,       max_length=__snake_case ,       pad_to_multiple_of=__snake_case ,       return_tensors='pt' ,       )

     # Instantiate dataloaders.
     __lowerCamelCase  :      Dict														=						DataLoader(
         tokenized_datasets['train'] ,       shuffle=__snake_case ,       collate_fn=__snake_case ,       batch_size=__snake_case				)
     __lowerCamelCase  :      List[Any]														=						DataLoader(
         tokenized_datasets['validation'] ,       shuffle=__snake_case ,       collate_fn=__snake_case ,       batch_size=__snake_case				)

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      A__						:   Tuple				=    mocked_dataloaders  # noqa: F811



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Union[str, Any]				)	->						Any:





     if os.environ.get('TESTING_MOCKED_DATALOADERS' ,       __snake_case				) == "1":
          __lowerCamelCase  :      Tuple														=						2
     # New Code #
     __lowerCamelCase  :      List[Any]														=						int(args.gradient_accumulation_steps				)
     # Initialize accelerator
     __lowerCamelCase  :      Union[str, Any]														=						Accelerator(
         cpu=args.cpu ,       mixed_precision=args.mixed_precision ,       gradient_accumulation_steps=__snake_case				)
     if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
          raise NotImplementedError(
              'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __lowerCamelCase  :      Optional[Any]														=						config['lr']
     __lowerCamelCase  :      Any														=						int(config['num_epochs']				)
     __lowerCamelCase  :      List[Any]														=						int(config['seed']				)
     __lowerCamelCase  :      List[str]														=						int(config['batch_size']				)

     __lowerCamelCase  :      List[str]														=						evaluate.load('glue' ,       'mrpc'				)

     set_seed(__snake_case				)
     __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						get_dataloaders(__snake_case ,       __snake_case				)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __lowerCamelCase  :      Tuple														=						AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,       return_dict=__snake_case				)

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     __lowerCamelCase  :      Any														=						model.to(accelerator.device				)

     # Instantiate optimizer
     __lowerCamelCase  :      Union[str, Any]														=						AdamW(params=model.parameters() ,       lr=__snake_case				)

     # Instantiate scheduler
     __lowerCamelCase  :      Dict														=						get_linear_schedule_with_warmup(
         optimizer=__snake_case ,       num_warmup_steps=1_00 ,       num_training_steps=(len(__snake_case				) * num_epochs) ,       )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						accelerator.prepare(
         __snake_case ,       __snake_case ,       __snake_case ,       __snake_case ,       __snake_case				)

     # Now we train the model
     for epoch in range(__snake_case				):
          model.train()
          for step, batch in enumerate(__snake_case				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device				)
               # New code #
               # We use the new `accumulate` context manager to perform gradient accumulation
               # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
               with accelerator.accumulate(__snake_case				):
                    __lowerCamelCase  :      List[str]														=						model(**__snake_case				)
                    __lowerCamelCase  :      List[Any]														=						output.loss
                    accelerator.backward(__snake_case				)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          for step, batch in enumerate(__snake_case				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device				)
               with torch.no_grad():
                    __lowerCamelCase  :      List[str]														=						model(**__snake_case				)
               __lowerCamelCase  :      Optional[Any]														=						outputs.logits.argmax(dim=-1				)
               __lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						accelerator.gather_for_metrics((predictions, batch['labels'])				)
               metric.add_batch(
                   predictions=__snake_case ,       references=__snake_case ,       )

          __lowerCamelCase  :      str														=						metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'epoch {epoch}:' ,       __snake_case				)



def      UpperCAmelCase__   (      )	->						Optional[int]:
     __lowerCamelCase  :      Tuple														=						argparse.ArgumentParser(description='Simple example of training script.'				)
     parser.add_argument(
         '--mixed_precision' ,       type=__snake_case ,       default=__snake_case ,       choices=['no', 'fp16', 'bf16', 'fp8'] ,       help='Whether to use mixed precision. Choose'
         'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
         'and an Nvidia Ampere GPU.' ,       )
     # New Code #
     parser.add_argument(
         '--gradient_accumulation_steps' ,       type=__snake_case ,       default=1 ,       help='The number of minibatches to be ran before gradients are accumulated.' ,       )
     parser.add_argument('--cpu' ,       action='store_true' ,       help='If passed, will train on the CPU.'				)
     __lowerCamelCase  :      List[str]														=						parser.parse_args()
     __lowerCamelCase  :      int														=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(__snake_case ,       __snake_case				)


if __name__ == "__main__":
      main()














"""simple docstring"""
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(a_)							,	"""Tatoeba directory does not exist.""")
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase_ )







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.resolver.convert_models(['heb-eng'] )







    @slow
    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.resolver.write_model_card('opus-mt-he-en'				,    dry_run=UpperCamelCase_ )
           assert mmeta["long_pair"] == "heb-eng"